When Shi et al. [1]} introduced the sub-pixel convolutional layer, they used it as the last layer of their CNN. Hence, the output depth of the upscaling layer is equal to the number of channels in the output image. Different from Shi et al. [1]}, we employ further convolutions after the upscaling layer. Nevertheless, since we are working with CT/MRI (grayscale) images, our final output has a single channel. In our architecture, the upscaling layer is followed by our second convolutional block, which starts with the seventh convolutional layer and ends with the tenth convolutional layer. The first three conv layers in our second block are formed of 32 filters. The tenth conv layer contains a single convolutional filter, since our output is a grayscale image that has a single channel. The second convolutional block contains a short skip connection, from the seventh conv layer to the ninth conv layer. The spatial size of \(h_O \times w_O\)  components of the activation maps is preserved throughout the second convolutional block, where \(h_O\)  and \(w_O\)  are the height and the width of the output image \(O\) .
